Spatial scales are important for examining health of exploited fishery stocks and guiding management actions. However, information about the optimal spatial scale is still unclear for assessment of transit fisheries, such as elver fisheries of the American eel Anguilla rostrata. We applied a generalized depletion model to assess catch and effort data from three nearby rivers (within 50 km) to test the hypothesis that modeling on pooled and separate data from nearby rivers would give similar estimates of abundance and exploitation rate. Overall, pooling data from rivers within 50 km did not result in large differences (<20% in relative difference) in estimates of abundance and exploitation rate with close mean abundance estimates and similar temporal trends in abundance, exploitation rate, and relative escapement. Pooling nearby river systems can greatly reduce modeling effort, at the cost of ignoring fine-scale variability in elver recruitment and having coarser spatial scale for the management. When only an index of annual recruitment and exploitation rate are of interest, pooling data may be practical from different locations up to 50 km.

中文翻译：

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汇集还是不汇集数据？应用广义耗竭模型评估加拿大新斯科舍省多条河流的美国鳗鲡渔业

空间尺度对于检查已开发渔业资源的健康状况和指导管理行动非常重要。然而，关于过境渔业评估的最佳空间尺度信息仍不清楚，例如美国鳗鲡的幼鱼渔业。我们应用广义消耗模型来评估附近三条河流（50 公里以内）的捕捞量和努力量数据，以检验以下假设：对附近河流的汇总数据和单独数据进行建模将给出相似的丰度和开采率估计值。总体而言，汇集 50 公里范围内河流的数据并没有导致丰度和开采率估计值存在较大差异（相对差异<20%），平均丰度估计值接近，丰度、开采率和相对逃逸量的时间趋势相似。汇集附近的河流系统可以大大减少建模工作，但代价是忽略幼虫招募的精细尺度变化以及管理的粗略空间尺度。当仅对年度招募和剥削率指数感兴趣时，汇集来自不同地点（最多 50 公里）的数据可能是实用的。